1. Field of the Invention
The present invention relates generally to the processing of video images, and more particularly to techniques for deinterlacing video images.
2. Description of the Related Art
The world's major television standards use a raster scanning technique known as “interlacing”. Interlaced scanning draws horizontal scan lines from the top of the screen to the bottom of the screen in two separate passes, one for even numbered scan lines and the other for odd numbered scan lines. Each of these passes is known as a field.
It is often necessary and/or desirable to convert an interlaced video signal to a progressively scanned signal. A single image or frame of a progressively scanned video source is scanned from the top of the screen to the bottom in a single pass. Such a conversion is advantageous, for example, when using a progressive scan display. Many of the display devices used to show interlaced video signals are actually progressive in nature. Common examples of such progressive displays are computer CRTs and liquid crystal displays (LCDs). One advantage of such displays is that they eliminate many of the deficiencies of interlaced video formats, such as flicker, line twitter, and visible scan line structure. Another advantage of the progressive scan format is improvement in the compression ratio of a digital format, such as a digital broadcast or satellite transmission.
In order for such an interlaced video image to be utilized by a progressively scanned video system, it must be converted from an interlaced scan format to a progressive scan format. There are a number of standard techniques for performing this conversion, and range from simple merging of adjacent fields, to interpolation from a single field to form a progressive frame, to more complex motion-adaptive or motion-compensation techniques.
The majority of these techniques require that new pixels be calculated to ensure the deinterlaced output is temporally consistent and has no interlace motion artifacts. The methods used for calculating new pixel information vary widely. Many prior art approaches to the calculation of new pixels are based strictly on vertically aligned pixel data from the temporally current field. Examples of this are shown in FIG. 1, which depicts the specific pixels within a surrounding pixel matrix 10 utilized to calculate a new pixel. FIG. 1A illustrates the calculation of a new pixel value 12 from a vertically aligned pair of pixels 14 by a simple average 16, and FIG. 1B illustrates the calculation of a new pixel 18 from four vertically aligned pixels 20 via a cubic polynomial curve fit 22. These vertically aligned pixel calculation techniques results in a loss of vertical resolution in the output image since computed pixels are based only on a single video field. Since the human visual system is very sensitive to object edges or boundaries, this loss of vertical detail is often very noticeable on diagonal edges of objects, which appear to have a jagged or ‘staircased’ appearance. An alternate technique used to calculate new pixel values is to take the median value of a number of pixels in the immediate area surrounding the pixel to be calculated, but this approach has met with limited success and often does not remedy the loss of vertical detail.
Prior art approaches to improving deinterlacer performance by eliminating jagged edges have typically involved some degree of detection of diagonal image features and subsequent computation of new pixels based on the temporally current image data along the detected feature. One method, disclosed in U.S. patent application Ser. No. 09/396,993, “Method and Apparatus for Detecting and Smoothing Diagonal Features in Video Images”, calculates the slope of a diagonal image feature based on the differences between pairs of pixels in a set of 4 pixels located along 45 degree angles with respect to the pixel to be calculated. As shown in FIG. 1C, a new pixel value 24 is calculated from a group of six surrounding pixels 26 by taking the average 28 of the two pixels located along either a positive 45 degree angle, a negative 45 degree angle, or a vertical angle, based on the calculated slope. Yet another method, disclosed in U.S. Pat. No. 5,936,676 calculates the magnitude of differences between pixel pairs along certain specific angles through the desired pixel location, and determines if a diagonal image feature is present and its orientation based on these differences. As shown in FIG. 1D, a new pixel 30 is then calculated from a group of surrounding pixels 32 by taking the average 34 of the pair of pixels located along the detected angle. Similar techniques (i.e., using the magnitude of pixel pair differences) are also described in U.S. Pat. Nos. 5,638,139 and 6,118,488. U.S. Pat. No. 6,133,957 describes yet another technique which computes the variance between sets of pixels centered along diagonals through the pixel location to be calculated and on opposite sides of the pixel location to be calculated, analyzes the resultant variances for multiple diagonals to identify a diagonal image feature direction as well as a measure of ambiguity of that direction, and then uses that information to compute a new pixel value based on other pixels located along or near the identified direction.
All of these techniques improve deinterlaced image quality to a certain degree, and some have met with more success than others. Some yield objectionable artifacts when the image feature detection either fails completely or yields an erroneous result. All of the disclosed techniques suffer to some extent from the inability to recognize larger image features since all base image feature detection on a relatively small area around the calculated pixel position.